2 posts tagged with "orchestration"

There's a pattern that plays out repeatedly when teams graduate from single-agent to multi-agent AI systems: individual agents work beautifully in isolation, but the system as a whole behaves unpredictably. The agents aren't the problem. The boundaries between them are.

Studies across production multi-agent deployments report failure rates ranging from 41% to 86.7% without formal orchestration. The most common post-mortem finding isn't "the LLM gave a bad answer" — it's "the wrong context reached the wrong agent at the wrong time." The seams between agents are where systems quietly fall apart.

One company shipped 7,949 AI agents. Fifteen percent of them worked. The rest failed silently, looped endlessly, or contradicted themselves mid-task. This is not a fringe result — enterprise analyses consistently find that 88% of AI agent projects never reach production, and 95% of generative AI pilots fail or severely underperform. The gap between a compelling demo and a reliable system is not a model problem. It is an architecture problem.

The engineers who are shipping agents that actually work have converged on a set of structural decisions that look nothing like the toy examples in framework tutorials. This post is about those decisions: where the layers are, where failures concentrate, and why the hardest problems are not about prompts.